## The Basics of LiDAR - Light Detection and Ranging - Remote Sensing

Last modified: Mar 29, 2017Explore the basics of how a LiDAR system works and what a LiDAR system measures.

Source:
National Ecological Observatory Network (NEON)

The tutorials in this series introduces Light Detection and Ranging (LiDAR).

Concepts covered include how LiDAR data is collected, LiDAR as gridded, raster
data and an introduction to digital models derived from LiDAR data (Canopy
Height Models (CHM), Digital Surface Models (DSM), and Digital Terrain Models
(DTM)). The series introduces the concepts through videos, graphical examples,
and text. The series continues with visualization of LiDAR-derived raster data
using plas.io, plot.ly and R, three free, open-source tools.

Data used in this series are from the National Ecological Observatory Network (NEON) and are in .las, GeoTiff and .csv formats.

After completing the series you will:

- Know what LiDAR data are
- Understand key attributes of LiDAR data
- Know what LiDAR-derived DTM, DSM, and CHM digital models are
- Be able to visualize LiDAR-derived data in .las format using plas.io
- Be able to create a Canopy Height Model in R
- Be able to create an interactive plot.ly map of LiDAR-derived data

To complete some of the tutorials in this series, you will need an updated
version of `R`

and, preferably, RStudio installed on your computer.

R
is a programming language that specializes in statistical computing. It is a
powerful tool for exploratory data analysis. To interact with `R`

, we strongly
recommend
RStudio,
an interactive development environment (IDE).

Data is available for download in those tutorials that focus on teaching data skills.

**Set Working Directory:** This lesson assumes that you have set your working
directory to the location of the downloaded and unzipped data subsets. An overview
of setting the working directory in `R`

can be found here.

**R Script & Challenge Code:** NEON data lessons often contain challenges that reinforce
learned skills. If available, the code for challenge solutions is found in the
downloadable `R`

script of the entire lesson, available in the footer of each lesson page.

Explore the basics of how a LiDAR system works and what a LiDAR system measures.

Understand LiDAR data product formats and learn the basics of how a LiDAR data are processed.

Learn about LiDAR point cloud file formats .las and .laz. Explore LiDAR point cloud data using the free, online Plas.io viewer .

In this tutorial, you will bring LiDAR-derived raster data (DSM and DTM) into R to create a canopy height model (CHM).

Learn to extract data from a raster using circular or square buffers created around a x,y location or from a shapefile. With this will will learn to convert x,y locations in a .csv file into a SpatialPointsDataFrame so that they can be

An overview of the basics needed to begin to exploring converting point data into raster or gridding format.